optimum_J_Wiki.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "from scipy.integrate import odeint\n",
    "from types import *\n",
    "import matplotlib.animation as animation\n",
    "from matplotlib import rc\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sum(di)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NoDrug_DesignResults.csv', 'Drug2_3000nM_DesignResults.csv', 'Drug6_3000nM_DesignResults.csv', 'Drug10_10000nM_DesignResults.csv', 'Drug5_300nM_DesignResults.csv', 'Drug5_10000nM_DesignResults.csv', 'Drug1_100nM_DesignResults.csv', 'Drug3_1000nM_DesignResults.csv', 'Drug7_1000nM_DesignResults.csv', 'Drug2_10000nM_DesignResults.csv', 'Drug5_100nM_DesignResults.csv', 'Drug1_300nM_DesignResults.csv', 'Drug9_100nM_DesignResults.csv', 'Drug6_1000nM_DesignResults.csv', 'Drug2_300nM_DesignResults.csv', 'Drug2_1000nM_DesignResults.csv', 'Drug6_100nM_DesignResults.csv', 'Drug7_10000nM_DesignResults.csv', 'Drug1_1nM_DesignResults.csv', 'Drug8_10000nM_DesignResults.csv', 'Drug7_3000nM_DesignResults.csv', 'Drug2_100nM_DesignResults.csv', 'Drug6_300nM_DesignResults.csv', 'Drug9_300nM_DesignResults.csv', 'Drug3_3000nM_DesignResults.csv', 'Drug8_1000nM_DesignResults.csv', 'Drug4_100nM_DesignResults.csv', 'Drug1_10000nM_DesignResults.csv', 'Drug10_100nM_DesignResults.csv', 'Drug9_10000nM_DesignResults.csv', 'Drug4_1000nM_DesignResults.csv', 'Drug9_3000nM_DesignResults.csv', 'Drug10_1000nM_DesignResults.csv', 'Drug4_300nM_DesignResults.csv', 'Drug6_10000nM_DesignResults.csv', 'Drug10_300nM_DesignResults.csv', 'Drug1_3000nM_DesignResults.csv', 'Drug5_3000nM_DesignResults.csv', 'Drug8_3000nM_DesignResults.csv', 'Drug4_3000nM_DesignResults.csv', 'Drug3_10000nM_DesignResults.csv', 'Drug8_300nM_DesignResults.csv', 'Drug7_300nM_DesignResults.csv', 'Drug3_100nM_DesignResults.csv', 'Drug4_10000nM_DesignResults.csv', 'Drug10_3000nM_DesignResults.csv', 'Drug9_1000nM_DesignResults.csv', 'Drug7_100nM_DesignResults.csv', 'Drug3_300nM_DesignResults.csv', 'Drug5_1000nM_DesignResults.csv', 'Drug1_1000nM_DesignResults.csv', 'Drug8_100nM_DesignResults.csv']\n",
      "      Drug  Concentration\n",
      "0    Drug1        14300.0\n",
      "1   Drug10        14600.0\n",
      "2    Drug2         4500.0\n",
      "3    Drug3        13300.0\n",
      "4    Drug4        36000.0\n",
      "5    Drug5        14400.0\n",
      "6    Drug6        20900.0\n",
      "7    Drug7        10501.0\n",
      "8    Drug8         6400.0\n",
      "9    Drug9         6100.0\n",
      "10  NoDrug         3000.0\n"
     ]
    }
   ],
   "source": [
    "pathName = \"TrueData\"\n",
    "\n",
    "# List all files in the directory\n",
    "all_files = os.listdir(pathName)\n",
    "\n",
    "# Filter out non-CSV files\n",
    "csv_files = [f for f in all_files if f.endswith('.csv')]\n",
    "print(csv_files)\n",
    "\n",
    "drug_names = []\n",
    "conc2 = []\n",
    "di_data = []\n",
    "for file in csv_files:\n",
    "    name = file.split('_')[0]\n",
    "    drug_names.append(name)\n",
    "\n",
    "    if name == \"NoDrug\":\n",
    "        concentration = 0\n",
    "    else:\n",
    "        concentration = file.split('_')[1]\n",
    "\n",
    "        #from concentration remove 2 last characters\n",
    "        concentration = concentration[:-2]\n",
    "        conc2.append(concentration)\n",
    "di_data.append(drug_names)\n",
    "di_data.append(conc2)\n",
    "\n",
    "# group di_data by drug and sum all concentrations\n",
    "di_data = pd.DataFrame(di_data).T\n",
    "di_data.columns = ['Drug', 'Concentration']\n",
    "di_data['Concentration'] = di_data['Concentration'].astype(float)\n",
    "di_data = di_data.groupby('Drug').sum().reset_index()\n",
    "print(di_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['NoDrug' 'Drug2' 'Drug6' 'Drug10' 'Drug5' 'Drug5' 'Drug1' 'Drug3'\n",
      "  'Drug7' 'Drug2' 'Drug5' 'Drug1' 'Drug9' 'Drug6' 'Drug2' 'Drug2' 'Drug6'\n",
      "  'Drug7' 'Drug1' 'Drug8' 'Drug7' 'Drug2' 'Drug6' 'Drug9' 'Drug3' 'Drug8'\n",
      "  'Drug4' 'Drug1' 'Drug10' 'Drug9' 'Drug4' 'Drug9' 'Drug10' 'Drug4'\n",
      "  'Drug6' 'Drug10' 'Drug1' 'Drug5' 'Drug8' 'Drug4' 'Drug3' 'Drug8'\n",
      "  'Drug7' 'Drug3' 'Drug4' 'Drug10' 'Drug9' 'Drug7' 'Drug3' 'Drug5'\n",
      "  'Drug1' 'Drug8']\n",
      " ['0' '3000nM' '3000nM' '10000nM' '300nM' '10000nM' '100nM' '1000nM'\n",
      "  '1000nM' '10000nM' '100nM' '300nM' '100nM' '1000nM' '300nM' '1000nM'\n",
      "  '100nM' '10000nM' '1nM' '10000nM' '3000nM' '100nM' '300nM' '300nM'\n",
      "  '3000nM' '1000nM' '100nM' '10000nM' '100nM' '10000nM' '1000nM' '3000nM'\n",
      "  '1000nM' '300nM' '10000nM' '300nM' '3000nM' '3000nM' '3000nM' '3000nM'\n",
      "  '10000nM' '300nM' '300nM' '100nM' '10000nM' '3000nM' '1000nM' '100nM'\n",
      "  '300nM' '1000nM' '1000nM' '100nM']\n",
      " [0.916674 0.302084 0.309861 0.091739 0.805724 0.090909 0.81147 0.498971\n",
      "  0.939004 0.093052 0.948662 0.726642 0.828182 0.826791 0.795105 0.795071\n",
      "  1.017138 0.099378 0.896289 0.090909 0.386709 0.85197 1.047916 0.712371\n",
      "  0.247911 0.503881 0.870526 0.090909 1.006178 0.090909 0.802167 0.253345\n",
      "  0.81524 0.755375 0.090909 0.862368 0.250858 0.250983 0.250803 0.291304\n",
      "  0.090909 0.760391 0.781936 1.019434 0.090909 0.300166 0.50522 0.874058\n",
      "  0.687083 0.500387 0.502277 0.88181]]\n"
     ]
    }
   ],
   "source": [
    "pathName = \"TrueData\"\n",
    "\n",
    "# List all files in the directory\n",
    "all_files = os.listdir(pathName)\n",
    "\n",
    "# Filter out non-CSV files\n",
    "csv_files = [f for f in all_files if f.endswith('.csv')]\n",
    "\n",
    "# Initialize an empty list to hold the DataFrames\n",
    "data_all = []\n",
    "drugs = []\n",
    "conc = []\n",
    "J = []\n",
    "# Loop through the list of CSV files and read each one into a DataFrame\n",
    "for file in csv_files:\n",
    "    file_path = os.path.join(pathName, file)\n",
    "    df = pd.read_csv(file_path)\n",
    "    \n",
    "\n",
    "    # take first part of file name as the name of the data\n",
    "    name = file.split('_')[0]\n",
    "\n",
    "    drugs.append(name)\n",
    "\n",
    "    if name == \"NoDrug\":\n",
    "        conc.append(0)\n",
    "    else:\n",
    "        conc.append((file.split('_')[1]))\n",
    "\n",
    "    vitality = df.groupby('Time')['is_cell_vital'].mean()\n",
    "    protein = df.groupby('Time')['Cyto_Intensity_1'].mean()\n",
    "\n",
    "    # take first value of vitality as the initial value\n",
    "    vitality0 = np.array(vitality)[0]\n",
    "    protein0 = np.array(protein)[0]\n",
    "    \n",
    "    # divide all values by the initial value\n",
    "    f = vitality/vitality0\n",
    "    p = (protein0 - protein)/protein0\n",
    "\n",
    "    #find in di_data the concentration of the drug\n",
    "    \n",
    "    # j = last f\n",
    "    j = (f.iloc[-1] + p.iloc[-1]) / (1+ (df['drug_concentration'][0]))\n",
    "\n",
    "    J.append(j)\n",
    "\n",
    "data_all.append(drugs)\n",
    "#data_all.append(J)\n",
    "\n",
    "#change conc to characters\n",
    "conc = np.array(conc)\n",
    "conc = conc.astype(str)\n",
    "data_all.append(conc)\n",
    "\n",
    "# change J list to numbers and round them\n",
    "J = np.asarray(J, dtype=float)\n",
    "J = J.round(6)\n",
    "J = J.flatten()\n",
    "data_all.append(J)\n",
    "\n",
    "data_all = np.asarray(data_all, dtype=object)\n",
    "\n",
    "print(data_all)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Drug Concentration         J\n",
      "0    Drug1         300nM  0.896289\n",
      "1   Drug10         300nM  1.006178\n",
      "2    Drug2         300nM  0.851970\n",
      "3    Drug3         300nM  1.019434\n",
      "4    Drug4         300nM  0.870526\n",
      "5    Drug5         300nM  0.948662\n",
      "6    Drug6         300nM  1.047916\n",
      "7    Drug7         300nM  0.939004\n",
      "8    Drug8         300nM  0.881810\n",
      "9    Drug9         300nM  0.828182\n",
      "10  NoDrug             0  0.916674\n",
      "      Drug Concentration         J\n",
      "0   NoDrug             0  0.916674\n",
      "1    Drug2        3000nM  0.302084\n",
      "2    Drug6        3000nM  0.309861\n",
      "3   Drug10       10000nM  0.091739\n",
      "4    Drug5         300nM  0.805724\n",
      "5    Drug5       10000nM  0.090909\n",
      "6    Drug1         100nM  0.811470\n",
      "7    Drug3        1000nM  0.498971\n",
      "8    Drug7        1000nM  0.939004\n",
      "9    Drug2       10000nM  0.093052\n",
      "10   Drug5         100nM  0.948662\n",
      "11   Drug1         300nM  0.726642\n",
      "12   Drug9         100nM  0.828182\n",
      "13   Drug6        1000nM  0.826791\n",
      "14   Drug2         300nM  0.795105\n",
      "15   Drug2        1000nM  0.795071\n",
      "16   Drug6         100nM  1.017138\n",
      "17   Drug7       10000nM  0.099378\n",
      "18   Drug1           1nM  0.896289\n",
      "19   Drug8       10000nM  0.090909\n",
      "20   Drug7        3000nM  0.386709\n",
      "21   Drug2         100nM  0.851970\n",
      "22   Drug6         300nM  1.047916\n",
      "23   Drug9         300nM  0.712371\n",
      "24   Drug3        3000nM  0.247911\n",
      "25   Drug8        1000nM  0.503881\n",
      "26   Drug4         100nM  0.870526\n",
      "27   Drug1       10000nM  0.090909\n",
      "28  Drug10         100nM  1.006178\n",
      "29   Drug9       10000nM  0.090909\n",
      "30   Drug4        1000nM  0.802167\n",
      "31   Drug9        3000nM  0.253345\n",
      "32  Drug10        1000nM  0.815240\n",
      "33   Drug4         300nM  0.755375\n",
      "34   Drug6       10000nM  0.090909\n",
      "35  Drug10         300nM  0.862368\n",
      "36   Drug1        3000nM  0.250858\n",
      "37   Drug5        3000nM  0.250983\n",
      "38   Drug8        3000nM  0.250803\n",
      "39   Drug4        3000nM  0.291304\n",
      "40   Drug3       10000nM  0.090909\n",
      "41   Drug8         300nM  0.760391\n",
      "42   Drug7         300nM  0.781936\n",
      "43   Drug3         100nM  1.019434\n",
      "44   Drug4       10000nM  0.090909\n",
      "45  Drug10        3000nM  0.300166\n",
      "46   Drug9        1000nM  0.505220\n",
      "47   Drug7         100nM  0.874058\n",
      "48   Drug3         300nM  0.687083\n",
      "49   Drug5        1000nM  0.500387\n",
      "50   Drug1        1000nM  0.502277\n",
      "51   Drug8         100nM  0.881810\n"
     ]
    }
   ],
   "source": [
    "# group data_all by drug and find max J for each drug\n",
    "data_all = pd.DataFrame(data_all.T, columns = ['Drug', 'Concentration', 'J'])\n",
    "data_all['J'] = data_all['J'].astype(float)\n",
    "data_all2 = data_all.groupby('Drug').max().reset_index()\n",
    "print(data_all2)\n",
    "print(data_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove NoDrug and drug 1\n",
    "data_all2 = data_all2[data_all2.Drug != 'NoDrug']\n",
    "data_all2 = data_all2[data_all2.Drug != 'Drug1']\n",
    "data_all2 = data_all2[data_all2.Drug != 'Drug3']\n",
    "data_all2 = data_all2[data_all2.Drug != 'Drug5']\n",
    "data_all2 = data_all2[data_all2.Drug != 'Drug8']\n",
    "data_all2 = data_all2[data_all2.Drug != 'Drug9']\n",
    "\n",
    "order = data_all2.sort_values('Drug', ascending=False)"
   ]
  }
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